Should product innovations look simple or
complex?
The effects of visual complexity on
consumers’ comprehension of product
innovations
Peiyao Cheng, PhD candidate, School of Design, Hong Kong Polytechnic University. pei-yao.cheng@connect.polyu.hk
Ruth Mugge, Associate Professor, Faculty of Industrial Design Engineering, Delft University of Technology. R.Mugge@tudelft.nl
Abstract
Consumers often have difficulty understanding the really new functions of product innovations. This study explores the potential role of product appearance, and more specifically visual complexity, to improve consumers’ comprehension of product innovations. Because visual complexity is directly determined by designers, it is essential to equip designers with the knowledge of how visual complexity influences consumers’ comprehension. We propose that a visually complex product appearance will result in a state of congruity with really new functions of the product innovation. Our results reveal that due to this congruity for really new products, a visually complex product appearance can improve consumers’ comprehension of the product innovation in comparison to a visually simple product. For an incremental new product, no effects for visual complexity were found.
Product appearance; Product innovation; Consumer response; Visual Complexity.
Developing successful product innovations is crucial for companies (Dougherty, 1992). The success of product innovations does not only rely on how well companies develop them from a technological perspective but also depends on how consumers respond to them. Consumers hold complex attitudes towards product innovations. On the one hand, consumers can be attracted by the new benefits that product innovations bring. On the other hand, consumers can be reluctant to adopt product innovation due to the high complexity
(Calantone, Chan, & Cui, 2006). To form an attitude towards a product innovation, consumers need to comprehend the product innovation (Rogers, 1995).
Consumers gain a comprehension of the product innovation through processing and interpreting relevant information (Graeff, 1995). To facilitate consumers’ comprehension of product innovations, several strategies have been examined, such as product bundling (Reinders, Frambach, & Schoormans, 2010), visualization (Zhao, Hoeffler, & Dahl, 2009) and analogical learning (Gregan‐Paxton, Hibbard, Brunel, & Azar, 2002). Although these studies provide valuable suggestions to promote consumers’ comprehension, facilitating consumers’ comprehension through product appearance remained unexplored.
The role of product appearance has been well acknowledged, with no exception in the area of product innovations. Talke, Salomo, Wieringa, and Lutz (2009) demonstrated that a novel product appearance can stimulate sales performance. Another study further investigated the effect of a novel product appearance from a consumer’s perspective (Mugge & Dahl, 2013), and they found that the positive effect of a novel product appearance only works for incrementally new products. Instead, for really new products, a typical-looking product appearance can ease consumers’ learning cost and further enhance their overall evaluation because the typical-looking product appearance can be used as a reference which further activates the existing knowledge related to that product category. These studies provide evidence on the potential role of product appearance for reducing the expected time and effort that is needed to learn to effectively use a product innovation. However, whether product appearance could facilitate consumers’ comprehension remains uninvestigated. This study aims to fill this gap by exploring how product appearance could influence consumers’ comprehension of product innovations. Specifically, different from previous studies that examine the effect of novel product appearances (Mugge & Dahl, 2013; Talke et al., 2009), this study focuses on visual complexity of product appearances.
Visual complexity
Visual complexity describes the level of complexity of a pattern, a shape or an object. A large number of independent elements, which share few similarities, make a complex pattern (Berlyne, 1971). More specifically, Pieters, Wedel, and Batra (2010) identified the following factors to influence the visual complexity of advertisements, which are also applicable for product appearances: quantity of objects, irregularity of objects, dissimilarity of objects, details of objects, and irregularity and asymmetry of object arrangements. Visual complexity is found to influence consumer response in different ways. For example, a visually complex advertisement has been demonstrated to have positive effects on consumers’ attention, comprehension and attitude towards the advertisement (Pieters et al., 2010). In product design, it was found that visually complex products can be favored when consumers pay more attention to product function and quality (Creusen, Veryzer, & Schoormans, 2010).
In addition, it is important to explore the effects of visual complexity for the context of product innovations. When reviewing products in the market, it is common to find product innovations in different visual complexity levels. For instance, Dyson DC39 (figure 1a) adopts an innovative cyclone technology and it is embodied in a particularly complex appearance. Dyson DC39 is composed of many different objects that are irregular and dissimilar, and these different objects are arranged in an irregular manner, giving it a complex appearance. In contrast, Philips Airfryer (figure 1b) uses a simple appearance to communicate its innovative function: frying food without oil through integrating rapid air technology. Philips Airfryer is formed out of one regular overall shape that contains few details, giving it a simple appearance. These different ways of embodying innovative functions also call for more empirical research to uncover the effects of visual complexity on consumer response. Furthermore, it is important to investigate this research question because consumers may rely on product appearance to draw inferences of product functions when the information of functionality is ambiguous (Yamamoto & Lambert, 1994). Because visual complexity is directly determined by designers, it is essential to equip designers with the knowledge of how visual complexity influences consumers’ comprehension.
Figure 1a Dyson DC 39 Figure 1b Philips AirFryer
Innovation Types: INPs & RNPs
A product innovation implies that a product is introduced to the market with some novel elements (Chandy & Prabhu, 2011). Depending on the innovativeness of the novel elements, product innovations can be categorized into incrementally new products (INPs) and really new products (RNPs). INPs involve new benefits, features or improvements, which are based on current technologies and markets. RNPs integrate advanced technology that has rarely been used in the industry before (Garcia & Calantone, 2002; Song & Montoya‐ Weiss, 1998). RNPs tend to be totally new to the whole industry and market. For consumers, comprehension of INPs will not be a problem because consumers have accumulated sufficient knowledge and experience during daily usage of similar products. Conversely, comprehension of RNPs could become a challenge for consumers because the integration of new technology calls for totally new ways of thinking and usage patterns (Veryzer, 1998). The knowledge that is needed to understand the new technology goes beyond consumers’ current knowledge (Gatignon & Robertson, 1985). As a result, consumers may need to spend extra efforts to learn RNPs and to gain a comprehension of them. Such a comprehension is
highly crucial because it will determine consumers’ initial attitudes towards the RNPs, and further influence the adoption decision (Rogers, 1995). If consumers fail to comprehend a RNP, they may disregard it and decide not to adopt it.
Consumer Responses to Visual complexity of INPs & RNPs
To gain a comprehension of the really new functions of product innovations, consumers are involved in sequences of information searching and processing. During these activities, consumers may encounter various information conveyed by multiple elements which they need to integrate into an overall impression. To form such an impression, congruity among different information is crucial because the high congruity can be processed easily and contribute to a clear impression (Van Rompay, De Vries, & Van Venrooij, 2010), while incongruity will bring ambiguity (Van Rompay, Pruyn, & Tieke, 2009). Previous studies have empirically demonstrated the positive effects of congruity in terms of symbolic meaning between a shape and a slogan of a package (Van Rompay et al., 2009), between a shape and a typeface of a package (Van Rompay & Pruyn, 2011), and between the images and the textual content on a webpage (Van Rompay et al., 2010). In addition, congruity can also take different forms, such as the appearance and the functions of a product innovation. When encountering a product innovation, consumers face product appearance and product functions to gain an initial comprehension. Consumers use product appearance as a source to draw some inferences of the product’s functional performance (Bloch, 1995). When functional features correspond to the functional inferences drawn from product appearance, a state of congruity is created. This congruity can facilitate consumers’ comprehension, because consumers naturally expect such a congruity between product appearance and functional features (Hoegg & Alba, 2011). In contrast, when functional features conflict with the functional inferences drawn from product appearances, consumers will encounter an incongruity that demands more cognitive resources to process. When consumers have sufficient cognitive resources, this incongruity can be translated into enhanced evaluation of product functional performance. However, when consumers do not have ample cognitive resources, the enhancement on evaluation will not happen (Hoegg, Alba, & Dahl, 2010). In line with these findings, (in)congruity between product appearance and product functional features can influence consumer responses to product innovations. Considering the differences between INPs and RNPs, (in)congruity may have different effects depending on the innovation type. For RNPs, learning and understanding the really new functions will require great cognitive efforts of consumers. In this situation, consistent with Hoegg et al. (2010), congruity between product appearance and function will be helpful for consumers. Because the congruity can facilitate consumers’ processing (Van Rompay & Pruyn, 2011) and demand fewer cognitive efforts (Hoegg et al., 2010), more cognitive resources can be spent on understanding the really new functions, resulting in enhanced comprehension of the product innovation. Conversely, if an incongruity between the product appearance and function exists for a RNP, consumers need to spend extra cognitive efforts to deal with the
incongruity, resulting in fewer cognitive efforts to learn and understand the really new functions. As a consequence, consumers will have less comprehension of the RNP.
Based on the foregoing arguments, visual complexity of product innovation can impact consumer responses through influencing the (in)congruity between the visual complexity of the product appearance and the functional features of product innovations. More specifically, when encountering a visually complex product appearance, consumers may naturally associate this product with complex technologies and multiple functions (Norman, 1988). In other words, RNPs with really new functions correspond to consumers’ initial inferences drawn from visually complex appearances. Thus, consumers tend to perceive a higher congruity level between visually complex product appearance and a RNP, in comparison to visually simple product appearance and a RNP. As a result, this congruity can facilitate consumers’ comprehension by providing more available cognitive spaces for consumers to learn really functions. However, when seeing a RNP with a visually simple appearance, consumers may perceive a state of incongruity due to the combination of visually simple appearance and really new product functions. This incongruity asks for extra cognitive efforts, which will lead to fewer available cognitive resources to understand the really new functions, and thus in less comprehension of the product innovation.
However, because understanding the incrementally new function of an INP is within consumers’ capability, the presence of visual complexity will not influence consumers’ comprehension. Thus, this enhancement of comprehension is not likely to happen for an INP. Furthermore, for an INP, consumers are already familiar with the incrementally new product functions, due to which they may rely less on product appearance to draw the inferences on functional performance. Correspondingly, the following hypotheses are proposed:
H1: Visual complexity will moderate the relationship between innovation type and consumers’ comprehension. Specifically, for a RNP, a high level of visual complexity (versus a low level of visual complexity) increases consumers’ comprehension of the product innovation (H1a). For an INP, the level of visual complexity does not influence consumers’ comprehension (H1b).
H2: Visual complexity will moderate the relationship between innovation type and congruity level between product appearance and function. Specifically, for a RNP, a high level of visual complexity (versus a low level of visual complexity) increases the congruity level between product appearance and function (H2a). For an INP, the level of visual complexity does not influence the congruity level between product appearance and function (H2b). H3: For a RNP, the congruity level between product appearance and function mediates the relationship between the level of visual complexity and consumers’ comprehension.
Method
To test the hypotheses, an experiment was conducted. Specifically, one main study and two pre-tests were performed. In pre-test 1, textual descriptions of INPs and RNPs were tested to ensure differences in innovativeness. Different product appearances were tested in pre-test 2 to guarantee differences in visual complexity. In the main study, the textual descriptions of INPs and RNPs were combined with simple and complex product appearances, resulting in four different conditions. Moreover, to improve the generalizability of the study, stimuli were created for three product categories: hair dryer, iron, and kettle. These three product categories were selected because they are common consumer durables and thus all participants will have some basic knowledge about these products. Moreover, the current style of these product categories is relatively diverse, which suggests that it is feasible to create different levels of visual complexity while minimizing confounding effects.
Pre-test 1: INP versus RNP
To create stimuli for INPs and RNPs, textual descriptions were created for each product category. Following prior research (Moreau, Lehmann, & Markman, 2001; Mugge & Dahl, 2013), we changed a core element of the product category that is used widely (e.g., heated steam in an iron is replaced by ultrasound waves) to create RNPs with totally different functions that provide extra benefits. The creation of really new functions was based on new products and concepts that were found online. For the INPs, the texts described a new product for which the core element has not changed, but that does incorporate some new features, which provide better functional performance. Appendix A illustrates the textual descriptions for all three product categories.
To check the manipulation of the product innovations, a 2 (innovation type: INP vs. RNP) × 3 (three product categories: hair dryer, iron, and kettle) mixed design was performed, with innovation type as between-subject factor and product category as within-subject factor. Twenty-five participants (40% male, mean age= 26.88) were asked to rate one textual description from each product category. The order of presenting the textual descriptions was counterbalanced. To measure the innovativeness of the stimuli, participants were asked to respond to the three-item measure ((Moreau et al., 2001): 1) How different is this product from other products in this product category you currently know about? (1= “not at all different” to 7= “very different”); 2) How innovative do you perceive this product to be? (1= “not very innovative” to 7= “very innovative”); 3) To what extent would this product change the way you would use the product? (1= “not at all” to 7= “very much”). The reliability of this scale was satisfactory across all product categories, ranging from α=.80 to .86. A repeated ANOVA revealed that there was a significant difference between the INPs and RNPs in terms of innovativeness, F(1,23)=14.21, p<.05, which suggests the manipulation is successful. Table 1 reports the means of pre-test 1.
Table1 Results of pre-test 1: means for innovativeness for INP&RNP of three product categories
INP RNP
Hair dryer 2.78 4.54
Iron 3.47 4.85
Kettle 3.03 4.54
Pre-test 2: simple versus complex product appearance
For the manipulation of visual complexity of the product appearances, a total of five product appearances were created for each product category. All product appearances were created by a trained designer with a MSc degree in Industrial Design. First, a simple product appearance of each product category was created based on the typical appearance of this category. Subsequently, stimuli were designed by changing the complexity level while minimizing potential confounding effects on the typicality level of the product appearances. The design of all product appearances was made in the form of a 3D visualization because prior research has concluded that this is a proper way to represent product appearances while diminishing other influences (Mugge & Dahl, 2013; Veryzer & Hutchinson, 1998). All 3D visualizations were standardized in size, color, and details.
Next, the fifteen product appearances were tested by sixty participants (40% male, mean age = 21.87). All participants had a design background, which makes them sensitive to visual differences. A 5 (visual complexity levels) × 3 (product category) mixed design was performed, with visual complexity level as between-subject factor and product category as within-subject factor. Each participant was randomly assigned to one of five conditions and rated three product appearances from different product categories on various measures. Following Cox and Cox (2002), visual complexity level was measured with two 7-point scale items anchored by: “simple/complicated” and “not complex/complex” (Pearson’s r’s ranging from .53 to .63). In addition, to prevent other confounding effects, attractiveness, typicality and functionality were measured. To measure attractiveness of product appearances, the following two items were used: “unattractive/attractive” and “ugly/beautiful” (Pearson’s r’s ranging from .72 to .89). Typicality was measured by the three 7-point scale items (Veryzer & Hutchinson, 1998) anchored by: “bad/good example of the product category”, “not very/very typical for the product category” and “unusual/usual” (α’s ranging from .84 to .91). Because a visually complex product may trigger the expectation of increased functionalities (Creusen et al., 2010; Norman, 1988), functionality was assessed to check for such a confounding effect. Functionality was measured based on three 7-point scale items (Cox & Cox, 2002) anchored by: “not useful/useful”, “not functional/functional” and “not practical/practical” (α’s ranging from .71 to .89). The order of presenting the product appearances was randomized.
Analyses were conducted separately for each product category. One-way ANOVAs were conducted with visual complexity level as the independent variable, and the ratings on visual complexity, attractiveness, functionality and typicality as dependent variables. Results revealed that participants’ ratings of visual complexity of product appearances significantly
differed between the stimuli for all three product categories: hair dryer (F(4,55)=5.53,
p<.05), iron (F(4,55)=3.49, p<.05), and kettle (F(4,55)=3.37, p<.05). Subsequently,
participants’ ratings on attractiveness, typicality and functionality were analyzed. Based on these results, two product appearances were selected for each product category that demonstrated the largest difference on visual complexity but did not significantly differ with respect to typicality, attractiveness, and functionality (see Table 2). The selected stimuli can be found in Appendix B.
Table 2. Results of pre-test2: means for visual complexity, typicality, attractiveness and functionality of three product categories
Low visual complexity
High visual complexity
Hair Dryer Visual Complexity 2.27 3.45
Typicality 5.70 5.37
Attractiveness 3.95 4.10
Functionality 5.40 5.70
Iron Visual Complexity 2.73 4.21
Typicality 5.88 5.19
Attractiveness 3.90 3.57
Functionality 5.67 5.62
Kettle Visual Complexity 2.73 3.67
Typicality 5.79 4.92
Attractiveness 3.36 3.27
Functionality 5.70 5.51
Main study
Design and participants
The main study applied a 2(innovation type: INP vs. RNP)×2(visual complexity level: simple vs. complex)×3(product category: hair dryer, iron, and kettle) mixed design, with innovation type and visual complexity level as between-subjects factors and product category as within-subjects factor. Seventy-seven participants (42.9% male, mean age = 41.00) were collected from a consumer panel. Only participants below 55 years old were selected, because younger people generally have less difficulty in accepting new products (Loudon & Bitta, 1993).
Procedure and measurements
The textual descriptions in pretest 1 and the visualizations of pretest 2 were combined to create the stimuli used in main study. This resulted in four conditions for each product category and twelve combinations in total. Each participant was assigned to one of the four conditions and was asked to evaluate three product categories on several measurements. The order of presenting the products was counterbalanced.
Participants’ comprehension of product innovations was measured by asking participants to indicate to what degree they agreed with the following two statements (Reinders et al.,
2010): “After looking at the picture of the product and reading the description, I have a very solid understanding of how this product works” and “After looking at the picture of the product and reading the description, I completely understand the various features of this new product” from 1 (strongly disagree) to 7 (strongly agree; Pearson’s r’s ranging from .78 to .88). To measure the congruity level between the product function and appearance, we used the following three statements (adapted from Fleck & Quester, 2007): “The product appearance of this product is well matched with the functions”, “In my opinion, the function of this product is very well communicated through this product appearance” and “The product appearance and its functions of this product go well together” from 1 (strongly disagree) to 7 (strongly agree; α’sranging from .83 to .93). Moreover, to validate the success of manipulations, we included measures of innovativeness (α’s ranging from .83 to .93), and visual complexity level of product appearance (Pearson’s r’s ranging from .74 to .82). These measures were identical to those used in the pretests.
To avoid potential confounding effects, attractiveness and typicality of product appearances were also measured. Attractiveness of product appearance was assessed by two items: “ugly /beautiful” and “unattractive/attractive” (Pearson’s r’s ranging from .80 to .94). Typicality of product appearance was measured by rating one 7-point scale item “bad/good example of the product category”. Furthermore, consumer innovativeness and the design acumen dimension of the Centrality of Visual Product Aesthetics (Bloch, Brunel, & Arnold, 2003) were included in the main study, as these constructs can influence participants’ capability of understanding product innovations(Truong, Klink, Fort‐Rioche, & Athaide, 2014). Consumer innovativeness was measured by four 7-point scale items (Manning, Bearden, & Madden, 1995) (α=.82). Following Truong et al. (2014), design acumen was measured by two 7-points scale items (Pearson’s r=.75).
Results
Manipulation Check
To test whether the manipulation of innovation type was successful, 2×2×3 mixed ANOVA was conducted with innovation type, visual complexity level and product category as independent variables, and ratings of innovativeness as dependent variable. The result confirmed the success of manipulation of innovativeness (F(1,69)=84.73, p<.01). Furthermore, 2×2×3 mixed ANOVA was performed with ratings of visual complexity as the dependent variable. As intended, the results showed a significant difference between simple and complex product appearances on the level of visual complexity (F(1,69)=9.39, p<.01) (see Table 3 for the means). In addition, no significant differences were found between simple and complex product appearances in terms of attractiveness (F(1,69)=2.42, p>.10) and typicality (F(1,69)=0.27, p>.10), which further provided evidence for the successful manipulation of our stimuli.
Test of Hypotheses
H1: Effects of visual complexity on consumer comprehension
H1 states that a complex (vs. simple) product appearance will increase consumers’ comprehension of RNPs. To test this hypothesis, a 2×2×3 mixed-ANCOVA was conducted with visual complexity level and innovation type as independent variables, consumer comprehension as dependent variables and age, gender, consumer innovativeness and design acumen as covariates. Results showed a significant interaction effect between innovation type and visual complexity level on consumers’ comprehension (F(1,69)=7.12, p<.05). Across three product categories, participants reported greater comprehension of the RNP when the RNP had a product appearance that was visually complex than when it was simple (F(1,30)=5.18, p<.05; Msimple=4.75, Mcomplex=5.61). For INPs, no significant difference was found between the two visual complexity conditions (F(1,35)=5.18, p>.10). No other effects were found. These results provide support for H1. Table 3 provides an overview of the results of the main study.
Table 3. Results of main study: means for visual complexity, innovativeness, congruity level, and comprehension of three product categories
INP RNP
Low visual
complexity High visual complexity complexity Low visual High visual complexity
Hair Dryer Visual complexity level 1.77 2.45 2.57 3.33 Innovativeness 2.06 2.32 4.12 4.80 Congruity level 4.83 5.00 4.00 5.09 Comprehension 5.75 5.09 5.08 5.57 Iron Visual complexity level 2.36 3.19 2.52 2.84 Innovativeness 3.16 2.87 5.00 5.19 Congruity level 4.59 4.47 3.79 4.94 Comprehension 5.37 4.36 4.37 5.19 Kettle Visual complexity level 2.09 2.82 2.31 3.51 Innovativeness 2.61 2.76 4.45 4.13 Congruity level 4.37 4.56 3.43 4.74 Comprehension 5.98 5.63 4.97 5.73
It was hypothesized that visual complexity will moderate the relationship between innovation type and congruity level. To test this hypothesis, a 2×2×3 mixed-ANCOVA was conducted with visual complexity and innovation type as independent variables, congruity level as dependent variables and age, gender, consumer innovativeness and design acumen as covariates. Results revealed a significant main effect of visual complexity on congruity level (F(1,69)=5.68, p<.05). However, this effect was qualified by an interaction effect between innovation type and visual complexity level on congruity level (F(1,69)=4.07,
p<.05). Across three product categories, participants reported a higher congruity level when
the RNP had a product appearance that was visually complex than when it was simple (F(1,30)=10.52, p<.01; Msimple=3.74, Mcomplex=4.92). However, for INPs, visual complexity had no impact on the congruity level (F(1,35)=0.07, p>.10;Msimple=4.60, Mcomplex=4.68). No other effects were found. These results provide support for H2.
H3: Mediation role of congruity for RNPs
We hypothesized that congruity level would mediate the effect of visual complexity on consumers’ comprehension of RNPs but not for INPs. To test this hypothesis, we followed the procedure of Preacher and Hayes (2004) and conducted a moderated mediation analysis by applying bootstrap analysis (MODMED; model 8). Innovation type was included as an independent variable. Visual complexity was included as a moderator; and age, gender, consumer innovativeness and design acumen were included as covariates. For congruity level and consumers’ comprehension, participants’ ratings were averaged among the three product categories and included as mediator and dependent variable respectively. In support of H3, the results revealed that consumers’ comprehension was predicted by the congruity level (B=0.42, t=4.16, p<.01). Results further indicated a significant conditional indirect effect of visual complexity on consumers’ comprehension through congruity level at p<.05 level for RNP (95%CI, .16 to 1.11). However, for INPs, this indirect conditional effect was not significant (95%CI, -2.51 to 0.36).
Discussion
This study contributes to the literature by demonstrating the value of visual complexity in the product appearance for enhancing consumers’ comprehension of product innovations. Specifically, the findings show that for a RNP, a visually complex appearance can enhance consumers’ comprehension in comparison to a simple one. When encountering a RNP with a visually complex product appearance, consumers perceive a congruity between the complex appearance and the inferences this appearance evokes regarding advanced technologies and the really new functions from the innovation. This congruity will facilitate the consumers’ comprehension because it leaves more cognitive resources available for understanding the really new functions of a RNP. For INPs, ample cognitive resources are present and thus visual complexity does not affect consumers’ comprehension.
In line with Mugge and Dahl (2013), this study demonstrates the value of product appearance for consumers’ acceptance of RNPs. More importantly, this study contributes by not only exploring visual complexity as another attribute of product appearance, but also by considering a different underlying mechanism to facilitate consumers’ comprehension: congruity between appearance and function. By doing so, this study also demonstrates the mediating role of congruity between visually complex appearance and the really new functions of a RNP on consumers’ comprehension of RNPs.
This study also extends research on the relationship between the appearance and functions of a product. As demonstrated in previous studies, consumers expect a state of congruity between the appearance and functions of a product (Hoegg & Alba, 2011; Hoegg et al., 2010). Specifically, previous studies focused on the attractiveness of the product appearance and demonstrated that consumers expect more beautiful products to have better functional performance. Our findings extend this research by demonstrating that consumers also expect congruity between a visual complex appearance and really new functions.
Although this study is carefully prepared, there are still some limitations we would like to address. First, when making stimuli, we used 3D visualizations to represent different levels of visual complexity to minimize the influences of other factors, such as colors and materials, which may negatively influence the validity of this study. However, these standardized visualizations limited the visual complexity level due to the focus on black and white images and the sole manipulation of shape. Furthermore, while creating the stimuli we realized that more extreme variations on complexity would result in confounding effects (e.g., on typicality). To minimize these effects, our selected stimuli did not reach very high levels of visual complexity. For product innovations in the market, not only product shape, but also colors, materials and other factors will jointly influence the visual complexity of the appearance. This might result in higher levels of visual complexity. Due to the mediating role of congruity, the relationship among different features in the product appearance (e.g., shape, color, material) should be investigated to achieve a high level of congruity that can further facilitate consumers’ comprehension. Further research is necessary to investigate the effects of visual complexity caused by the joint effects of different features in the product appearance. Secondly, to ensure the generalizability of our study, we included three product categories from consumer durables. Although these categories greatly differ in the usage situation, they also share some similarities because they are all common products that consumers have some knowledge of, and therefore, require relatively low learning efforts for consumers (Mukherjee & Hoyer, 2001). Consequently, future research should replicate our findings using other product categories, such as product categories that are totally new to consumers. Because consumers require even higher cognitive learning efforts for those products, it would be interesting to examine the effect of visual complexity on consumers’ comprehension for those products as well, because the effects might also be more helpful. Thirdly, because consumers’ comprehension of product innovations calls for multiple exposures, repeated exposure is supposed to influence consumers’ comprehension positively.
Our study confirms that visually complex product innovation can enhance consumers’ comprehension at first sight, which is a precondition of the adoption decision. However, consumers’ attitudes towards visual complex stimuli will improve with multiple exposure (Cox & Cox, 2002). It would be beneficial to investigate the effects of visual complexity on consumer responses to RNPs over multiple exposures. This enables us to explore whether the enhancement in comprehension at first sight can be translated into improvements on consumers’ overall attitudes. Finally, we recognize the limitations of using an experimental approach to study the effects of visual complexity. In a market situation, many other aspects (e.g., retail, promotion, brand, peer discussions) can influence consumers’ comprehension of a product. Although our findings support the positive effects of visual complexity on product comprehension, we realize that product appearance is only one of the ways to do so, and companies should consider the whole set of options and their specific consequences when determining a market strategy for a RNP.
Conclusion
While developing RNPs, designers should carefully consider the influence of product appearance on consumers’ comprehension, because lack of comprehension can lead to rejection of RNPs. Nowadays, there is a trend towards simplicity in product design (Lockwood, 2015). Designers more often use simple product appearances while designing by including fewer details and different parts. However, our study demonstrates that visual complexity can be beneficial for designers when designing new product appearances as well. Visual complexity can improve consumers’ comprehension of RNPs through the congruity between appearance and function, thereby reducing one of the most important barriers for adopting innovations.
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Author Biographies
Peiyao Cheng
Peiyao Cheng is a PhD candidate in School of Design, Hong Kong Polytechnic University. Her research focuses on the influence of product appearance on consumer response to innovation. She has published her research in Design Studies and presented her work in DesignEd Asia Conference.
Ruth Mugge
Dr. Ruth Mugge is associate professor of consumer research in the Faculty of Industrial Design Engineering at Delft University of Technology. Her main research focus is on understanding consumer response to product design at purchase and during ownership. She has published her research in such journals as Acta Psychologica, Journal of Engineering Design, British Journal of Psychology, Design Studies, Applied Ergonomics, and
International Journal of Design. She received her PhD from the Delft University of Technology.
Appendix A: Textual descriptions of incrementally new product and really new product for three product categories
Incrementally New Product (INP) Really New Product (RNP)
Hair Dryer
The HD-X5 is a new hairdryer. This hairdryer incorporates a new engine with a higher wattage that provides more power. This will allow the hairdryer to produce more heat and to dry the hair faster. Furthermore, the hairdryer has three different speeds, comes with an add-on diffuser, and weighs 0.90 kg.
The HD-X5 is a new hairdryer. This hairdryer incorporates a new sensor that measures the dryness of the hair. This will allow the hairdryer to automatically adjust the temperature of the air accordingly. Furthermore, the hairdryer has three different air speeds, comes with an add-on diffuser, and weighs 0.90 kg.
Iron
JC-X3 is a new iron. This iron has a more powerful heating element to produce steam continuously. This will allow the iron to moisten the fabric evenly, and to remove the wrinkles in clothes faster. Furthermore, the iron has three different levels for producing steam and weighs 0.50 kg.
JC-X3 is a new iron. This iron produces ultrasound waves rather than steam to iron clothes. This will allow the iron to remove the wrinkles in clothes with little pressure. Furthermore, the iron has three different levels for producing ultrasound waves and weighs 0.50 kg.
Kettle
The KL-T3 is a new electrical kettle. This kettle incorporates a heating element with a higher wattage. This will allow the kettle to heat water in a much shorter time. Furthermore, the kettle has a safety system against short circuit and boil-dry, and it can contain 1.6 L water.
The KL-T3 is a new electrical kettle. This kettle incorporates an advanced technology that can produce UV rays. This will allow the kettle to purify water while heating water. Furthermore, the kettle has a safety system against short circuit and boil-dry, and it can contain 1.6 L water.
Appendix B: Product appearances incrementally new product and really new product for three product categories
Hair Dryer Iron Kettle
Low level of visual complexity
High level of visual complexity
